For the purposes of this paper, the vehicle was tele-operated from the nearby BS with the operator manoeuvring with the aid of three onboard video cameras and a display of the LiDAR data in realtime. Autonomous operation using the LiDAR data and was demonstrated in Whitty et al. (2010)(For videos, see our YouTube channel: www. youtube.com/UNSWMechatronics).

Jarrett’s wines, the subject location of this study, had undertaken much of the above however it soon became apparent that data was poorly managed, with a mix of hard- and soft-copy data. The importance of spatial data management is rarely reported in the PV literature, and the ad hoc nature of spatial data acquisition and surveyor involvement limits the opportunities for an effi cient spatial data management system to be implemented.

Precision viticulture (PV) is styled from the zonal management paradigm of precision agriculture, where large homogeneous fields are divided into smaller units based on yield or other field characteristics which may be differentially managed (Lamb et al., 2002, Bramley, 2009, Bramley and Robert, 2003) (Note that McBratney et al. (2005) suggest the definition of precision agriculture is continually evolving as we develop further technologies and greater awareness of agricultural processes). PV acknowledges the numerous spatial variations that affect grape quality and yield, including soil characteristics, pests and diseases and topography (Hall et al., 2003, Arnó et al., 2009), providing land managers with the tools to quantify and manage this variability (Proffitt, 2006). Land managers can thus ‘selectively’ treat areas, for example by the variable application of mulch, water, fertiliser, sprays etc.